Comparing disease control policies for interacting wild populations

We consider interacting population systems of predator-prey type, presenting four models of control strategies for epidemics among the prey. In particular to contain the transmissible disease, safety niches are considered, assuming they lessen the disease spread, but do not protect prey from predators. This represents a novelty with respect to standard ecosystems where the refuge prevents predators' attacks. The niche is assumed either to protect the healthy individuals, or to hinder the infected ones to get in contact with the susceptibles, or finally to reduce altogether contacts that might lead to new cases of the infection. In addition a standard culling procedure is also analysed. The effectiveness of the different strategies are compared. Probably the environments providing a place where disease carriers cannot come in contact with the healthy individuals, or where their contact rates are lowered, seem to preferable for disease containment

Scalable training on scalable infrastructures for programmable hardware

Machine learning (ML) and deep learning (DL) techniques are increasingly influential in High Energy Physics, necessitating effective computing infrastructures and training opportunities for users and developers, particularly concerning programmable hardware like FPGAs. A gap exists in accessible ML/DL on FPGA tutorials catering to diverse hardware specifications. To bridge this gap, collaborative efforts by INFN-Bologna, the University of Bologna, and INFN-CNAF produced a pilot course using virtual machines, inhouse cloud platforms, and AWS instances, utilizing Docker containers for interactive exercises. Additionally, the Bond Machine software ecosystem, capable of generating FPGA-synthesizable computer architectures, is explored as a simplified approach for teaching FPGA programming